With the outbreak of the "Year of AI Agents" in 2025, the popularity of the open-source AI framework OpenClaw (commonly known as "Lobster") has led to an unprecedented surge in edge-side AI computing power demand. In response to this trend, AMD recently introduced a revolutionary concept: "Agent Computer". AMD believes that in the AI era, users should have two computers: one for traditional office and daily life, and another as a "Cyber Workhorse," specifically responsible for running various AI agents around the clock.

Leaving the Cloud: The Economics and Security of Local "Raising Lobsters"

AMD points out that the current AI popularization faces three major barriers: deployment difficulty, privacy leaks, and high token costs. Especially for agents like "Lobster" that frequently call large models, relying entirely on the cloud can almost lead to uncontrolled costs.

Therefore, local deployment becomes the ultimate solution. To "raise" an AI agent with basic capabilities, the device needs at least 10GB of VRAM; if running multiple AI employees or loading a private knowledge base, VRAM requirements will jump above 64GB. Compared to buying several expensive dedicated graphics cards, AMD's Ryzen AI Max Series Platform offers a high-performance and low-cost "base": with 128GB of ultra-large unified memory, it can allocate up to 96GB of dedicated VRAM, allowing ordinary users to own a top-tier AI workstation for just over 20,000 yuan.

Agent Computer: A New Milestone in the History of Computing

AMD believes the essential difference between an Agent Computer and a traditional PC lies in the driving method. Traditional PCs are "human-computer interaction driven," requiring users to sit in front of the screen to operate. An Agent Computer, however, is "AI self-driven," requiring no keyboard, mouse, or monitor. Instead, it receives tasks and provides results through communication software such as WeChat and Slack anytime and anywhere.

Currently, solutions based on the Ryzen AI Max + 395 platform have been implemented in 11 industries, including finance, healthcare, and education. For example, in the medical field, local AI agents can integrate expert knowledge bases to assist doctors in completing medical records. In the education industry, they can transform massive papers into interactive dialogue entities. This "specialized machine for specific use" model marks our official transition from the mobile internet era into the "Decade of AI Agents."